TY - JOUR

T1 - First-Principles Multiscale Modeling of Mechanical Properties in Graphene/Borophene Heterostructures Empowered by Machine-Learning Interatomic Potentials

AU - Mortazavi, Bohayra

AU - Silani, Mohammad

AU - Podryabinkin, Evgeny V.

AU - Rabczuk, Timon

AU - Zhuang, Xiaoying

AU - Shapeev, Alexander V.

N1 - Funding Information: M.S. and E.V.P. contributed equally to this work. B.M. and X.Z. appreciate the funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy within the Cluster of Excellence PhoenixD (EXC 2122, Project ID 390833453). E.V.P and A.V.S. were supported by the Russian Science Foundation (Grant No 18-13-00479, https://rscf.ru/project/18-13-00479/). M.S. thanks the financial support of the Iran National Science Foundation (INSF) under project no. 940004. X.Z. acknowledges the sponsorship from the European Research Council Starting Grant COTOFLEXI (No. 802205). B.M. and T.R. are greatly thankful to the VEGAS cluster at Bauhaus University of Weimar for providing the computational resources. Open Access funding enabled and organized by Projekt DEAL.

PY - 2021/9/2

Y1 - 2021/9/2

N2 - Density functional theory calculations are robust tools to explore the mechanical properties of pristine structures at their ground state but become exceedingly expensive for large systems at finite temperatures. Classical molecular dynamics (CMD) simulations offer the possibility to study larger systems at elevated temperatures, but they require accurate interatomic potentials. Herein the authors propose the concept of first-principles multiscale modeling of mechanical properties, where ab initio level of accuracy is hierarchically bridged to explore the mechanical/failure response of macroscopic systems. It is demonstrated that machine-learning interatomic potentials (MLIPs) fitted to ab initio datasets play a pivotal role in achieving this goal. To practically illustrate this novel possibility, the mechanical/failure response of graphene/borophene coplanar heterostructures is examined. It is shown that MLIPs conveniently outperform popular CMD models for graphene and borophene and they can evaluate the mechanical properties of pristine and heterostructure phases at room temperature. Based on the information provided by the MLIP-based CMD, continuum models of heterostructures using the finite element method can be constructed. The study highlights that MLIPs were the missing block for conducting first-principles multiscale modeling, and their employment empowers a straightforward route to bridge ab initio level accuracy and flexibility to explore the mechanical/failure response of nanostructures at continuum scale.

AB - Density functional theory calculations are robust tools to explore the mechanical properties of pristine structures at their ground state but become exceedingly expensive for large systems at finite temperatures. Classical molecular dynamics (CMD) simulations offer the possibility to study larger systems at elevated temperatures, but they require accurate interatomic potentials. Herein the authors propose the concept of first-principles multiscale modeling of mechanical properties, where ab initio level of accuracy is hierarchically bridged to explore the mechanical/failure response of macroscopic systems. It is demonstrated that machine-learning interatomic potentials (MLIPs) fitted to ab initio datasets play a pivotal role in achieving this goal. To practically illustrate this novel possibility, the mechanical/failure response of graphene/borophene coplanar heterostructures is examined. It is shown that MLIPs conveniently outperform popular CMD models for graphene and borophene and they can evaluate the mechanical properties of pristine and heterostructure phases at room temperature. Based on the information provided by the MLIP-based CMD, continuum models of heterostructures using the finite element method can be constructed. The study highlights that MLIPs were the missing block for conducting first-principles multiscale modeling, and their employment empowers a straightforward route to bridge ab initio level accuracy and flexibility to explore the mechanical/failure response of nanostructures at continuum scale.

KW - first-principles calculations

KW - machine learning

KW - mechanical/failure response

KW - multiscale modeling

UR - http://www.scopus.com/inward/record.url?scp=85111038766&partnerID=8YFLogxK

U2 - 10.1002/adma.202102807

DO - 10.1002/adma.202102807

M3 - Article

C2 - 34296779

AN - SCOPUS:85111038766

VL - 33

JO - Advanced materials

JF - Advanced materials

SN - 0935-9648

IS - 35

M1 - 2102807

ER -